Created on Cognitive Class Labs

DB0201EN-Week3-1-4-Analyzing-v5-py.ipynb

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   "source": [
    "<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/ugcqz6ohbvff804xp84y4kqnvvk3bq1g.png\" width = 300, align = \"center\"></a>\n",
    "\n",
    "<h1 align=center><font size = 5>Lab: Analyzing a real world data-set with SQL and Python</font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "This notebook shows how to store a dataset into a database using and analyze data using SQL and Python. In this lab you will:\n",
    "1. Understand a dataset of selected socioeconomic indicators in Chicago\n",
    "1. Learn how to store data in an Db2 database on IBM Cloud instance\n",
    "1. Solve example problems to practice your SQL skills "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selected Socioeconomic Indicators in Chicago\n",
    "\n",
    "The city of Chicago released a dataset of socioeconomic data to the Chicago City Portal.\n",
    "This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” for each Chicago community area, for the years 2008 – 2012.\n",
    "\n",
    "Scores on the hardship index can range from 1 to 100, with a higher index number representing a greater level of hardship.\n",
    "\n",
    "A detailed description of the dataset can be found on [the city of Chicago's website](\n",
    "https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2), but to summarize, the dataset has the following variables:\n",
    "\n",
    "* **Community Area Number** (`ca`): Used to uniquely identify each row of the dataset\n",
    "\n",
    "* **Community Area Name** (`community_area_name`): The name of the region in the city of Chicago \n",
    "\n",
    "* **Percent of Housing Crowded** (`percent_of_housing_crowded`): Percent of occupied housing units with more than one person per room\n",
    "\n",
    "* **Percent Households Below Poverty** (`percent_households_below_poverty`): Percent of households living below the federal poverty line\n",
    "\n",
    "* **Percent Aged 16+ Unemployed** (`percent_aged_16_unemployed`): Percent of persons over the age of 16 years that are unemployed\n",
    "\n",
    "* **Percent Aged 25+ without High School Diploma** (`percent_aged_25_without_high_school_diploma`): Percent of persons over the age of 25 years without a high school education\n",
    "\n",
    "* **Percent Aged Under** 18 or Over 64:Percent of population under 18 or over 64 years of age (`percent_aged_under_18_or_over_64`): (ie. dependents)\n",
    "\n",
    "* **Per Capita Income** (`per_capita_income_`): Community Area per capita income is estimated as the sum of tract-level aggragate incomes divided by the total population\n",
    "\n",
    "* **Hardship Index** (`hardship_index`): Score that incorporates each of the six selected socioeconomic indicators\n",
    "\n",
    "In this Lab, we'll take a look at the variables in the socioeconomic indicators dataset and do some basic analysis with Python.\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to the database\n",
    "Let us first load the SQL extension and establish a connection with the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Connected: sgn89104@BLUDB'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# Remember the connection string is of the format:\n",
    "# %sql ibm_db_sa://my-username:my-password@my-hostname:my-port/my-db-name\n",
    "# Enter the connection string for your Db2 on Cloud database instance below\n",
    "# i.e. copy after db2:// from the URI string in Service Credentials of your Db2 instance. Remove the double quotes at the end.\n",
    "%sql ibm_db_sa://sgn89104:5%2B4crpdcc43ksb3j@dashdb-txn-sbox-yp-lon02-01.services.eu-gb.bluemix.net:50000/BLUDB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store the dataset in a Table\n",
    "##### In many cases the dataset to be analyzed is available as a .CSV (comma separated values) file, perhaps on the internet. To analyze the data using SQL, it first needs to be stored in the database.\n",
    "\n",
    "##### We will first read the dataset source .CSV from the internet into pandas dataframe\n",
    "\n",
    "##### Then we need to create a table in our Db2 database to store the dataset. The PERSIST command in SQL \"magic\" simplifies the process of table creation and writing the data from a `pandas` dataframe into the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://sgn89104:***@dashdb-txn-sbox-yp-lon02-01.services.eu-gb.bluemix.net:50000/BLUDB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Persisted chicago_socioeconomic_data'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "chicago_socioeconomic_data = pandas.read_csv('https://data.cityofchicago.org/resource/jcxq-k9xf.csv')\n",
    "%sql PERSIST chicago_socioeconomic_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### You can verify that the table creation was successful by making a basic query like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://sgn89104:***@dashdb-txn-sbox-yp-lon02-01.services.eu-gb.bluemix.net:50000/BLUDB\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>index</th>\n",
       "        <th>ca</th>\n",
       "        <th>community_area_name</th>\n",
       "        <th>hardship_index</th>\n",
       "        <th>per_capita_income_</th>\n",
       "        <th>percent_aged_16_unemployed</th>\n",
       "        <th>percent_aged_25_without_high_school_diploma</th>\n",
       "        <th>percent_aged_under_18_or_over_64</th>\n",
       "        <th>percent_households_below_poverty</th>\n",
       "        <th>percent_of_housing_crowded</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>0</td>\n",
       "        <td>1.0</td>\n",
       "        <td>Rogers Park</td>\n",
       "        <td>39.0</td>\n",
       "        <td>23939</td>\n",
       "        <td>8.7</td>\n",
       "        <td>18.2</td>\n",
       "        <td>27.5</td>\n",
       "        <td>23.6</td>\n",
       "        <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>2.0</td>\n",
       "        <td>West Ridge</td>\n",
       "        <td>46.0</td>\n",
       "        <td>23040</td>\n",
       "        <td>8.8</td>\n",
       "        <td>20.8</td>\n",
       "        <td>38.5</td>\n",
       "        <td>17.2</td>\n",
       "        <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>2</td>\n",
       "        <td>3.0</td>\n",
       "        <td>Uptown</td>\n",
       "        <td>20.0</td>\n",
       "        <td>35787</td>\n",
       "        <td>8.9</td>\n",
       "        <td>11.8</td>\n",
       "        <td>22.2</td>\n",
       "        <td>24.0</td>\n",
       "        <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>3</td>\n",
       "        <td>4.0</td>\n",
       "        <td>Lincoln Square</td>\n",
       "        <td>17.0</td>\n",
       "        <td>37524</td>\n",
       "        <td>8.2</td>\n",
       "        <td>13.4</td>\n",
       "        <td>25.5</td>\n",
       "        <td>10.9</td>\n",
       "        <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>4</td>\n",
       "        <td>5.0</td>\n",
       "        <td>North Center</td>\n",
       "        <td>6.0</td>\n",
       "        <td>57123</td>\n",
       "        <td>5.2</td>\n",
       "        <td>4.5</td>\n",
       "        <td>26.2</td>\n",
       "        <td>7.5</td>\n",
       "        <td>0.3</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(0, 1.0, 'Rogers Park', 39.0, 23939, 8.7, 18.2, 27.5, 23.6, 7.7),\n",
       " (1, 2.0, 'West Ridge', 46.0, 23040, 8.8, 20.8, 38.5, 17.2, 7.8),\n",
       " (2, 3.0, 'Uptown', 20.0, 35787, 8.9, 11.8, 22.2, 24.0, 3.8),\n",
       " (3, 4.0, 'Lincoln Square', 17.0, 37524, 8.2, 13.4, 25.5, 10.9, 3.4),\n",
       " (4, 5.0, 'North Center', 6.0, 57123, 5.2, 4.5, 26.2, 7.5, 0.3)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql SELECT * FROM chicago_socioeconomic_data limit 5;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problems\n",
    "\n",
    "### Problem 1\n",
    "\n",
    "##### How many rows are in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://sgn89104:***@dashdb-txn-sbox-yp-lon02-01.services.eu-gb.bluemix.net:50000/BLUDB\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>78</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(Decimal('78'),)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "%sql SELECT COUNT(*) FROM chicago_socioeconomic_data;\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Hint:\n",
    "\n",
    "%sql SELECT COUNT(*) FROM chicago_socioeconomic_data;\n",
    "\n",
    "Correct answer: 78\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 2\n",
    "\n",
    "##### How many community areas in Chicago have a hardship index greater than 50.0?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Hint:\n",
    "\n",
    "%sql SELECT COUNT(*) FROM chicago_socioeconomic_data WHERE hardship_index > 50.0;\n",
    "Correct answer: 38\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3\n",
    "\n",
    "##### What is the maximum value of hardship index in this dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Hint:\n",
    "\n",
    "%sql SELECT MAX(hardship_index) FROM chicago_socioeconomic_data;\n",
    "\n",
    "Correct answer: 98.0\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 4\n",
    "\n",
    "##### Which community area which has the highest hardship index?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Hint:\n",
    "\n",
    "## We can use the result of the last query to as an input to this query:\n",
    "%sql SELECT community_area_name FROM chicago_socioeconomic_data where hardship_index=98.0\n",
    "\n",
    "## or another option:\n",
    "%sql SELECT community_area_name FROM chicago_socioeconomic_data ORDER BY hardship_index DESC NULLS LAST FETCH FIRST ROW ONLY;\n",
    "\n",
    "## or you can use a sub-query to determine the max hardship index:\n",
    "%sql select community_area_name from chicago_socioeconomic_data where hardship_index = ( select max(hardship_index) from chicago_socioeconomic_data ) \n",
    "\n",
    "Correct answer: 'Riverdale'\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 5\n",
    "\n",
    "##### Which Chicago community areas have per-capita incomes greater than $60,000?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Hint:\n",
    "\n",
    "%sql SELECT community_area_name FROM chicago_socioeconomic_data WHERE per_capita_income_ > 60000;\n",
    "\n",
    "Correct answer:Lake View,Lincoln Park, Near North Side, Loop\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 6\n",
    "\n",
    "##### Create a scatter plot using the variables `per_capita_income_` and `hardship_index`. Explain the correlation between the two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://sgn89104:***@dashdb-txn-sbox-yp-lon02-01.services.eu-gb.bluemix.net:50000/BLUDB\n",
      "Done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "income_vs_hardship = %sql SELECT per_capita_income_, hardship_index FROM chicago_socioeconomic_data;\n",
    "plot = sns.jointplot(x='per_capita_income_',y='hardship_index', data=income_vs_hardship.DataFrame())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Hint:\n",
    "# if the import command gives ModuleNotFoundError: No module named 'seaborn'\n",
    "# then uncomment the following line i.e. delete the # to install the seaborn package \n",
    "# !pip install seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "income_vs_hardship = %sql SELECT per_capita_income_, hardship_index FROM chicago_socioeconomic_data;\n",
    "plot = sns.jointplot(x='per_capita_income_',y='hardship_index', data=income_vs_hardship.DataFrame())\n",
    "\n",
    "Correct answer:You can see that as Per Capita Income rises as the Hardship Index decreases. We see that the points on the scatter plot are somewhat closer to a straight line in the negative direction, so we have a negative correlation between the two variables. \n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "##### Now that you know how to do basic exploratory data analysis using SQL and python visualization tools, you can further explore this dataset to see how the variable `per_capita_income_` is related to `percent_households_below_poverty` and `percent_aged_16_unemployed`. Try to create interesting visualizations!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "##### In this lab you learned how to store a real world data set from the internet in a database (Db2 on IBM Cloud), gain insights into data using SQL queries. You also visualized a portion of the data in the database to see what story it tells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright &copy; 2018 [cognitiveclass.ai](cognitiveclass.ai?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
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  "widgets": {
   "state": {},
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 },
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}